Requirements
- No prior experience is required. We will start from the very basics
- You’ll need to install Anaconda. We will show you how to do that step by step
This course includes:
- Role Play
- 29.5 hours on-demand video
- 107 coding exercises
- 21 articles
- 143 downloadable resources
- Certificate of completion
Who this course is for:
- You should take this course if you want to become an AI Engineer or if you want to learn about the field
- This course is for you if you want a great career
- The course is also ideal for beginners, as it starts from the fundamentals and gradually builds up your skills
What you'll learn
- The course provides the entire toolbox you need to become an AI Engineer
- Understand key Artificial Intelligence concepts and build a solid foundation
- Start coding in Python and learn how to use it for NLP and AI
- Impress interviewers by showing an understanding of the AI field
- Apply your skills to real-life business cases
- Harness the power of Large Language Models
- Leverage LangChain for seamless development of AI-driven applications by chaining interoperable components
- Become familiar with Hugging Face and the AI tools it offers
- Use APIs and connect to powerful foundation models
- Utilize Transformers for advanced speech-to-text
Description
The Problem
AI Engineers are best suited to thrive in the age of AI. It helps businesses utilize Generative AI by building AI-driven applications on top of their existing websites, apps, and databases. Therefore, it’s no surprise that the demand for AI Engineers has been surging in the job marketplace.
Supply, however, has been minimal, and acquiring the skills necessary to be hired as an AI Engineer can be challenging.
So, how is this achievable?
Universities have been slow to create specialized programs focused on practical AI Engineering skills. The few attempts that exist tend to be costly and time-consuming.
Most online courses offer ChatGPT hacks and isolated technical skills, yet integrating these skills remains challenging.
The Solution
AI Engineering is a multidisciplinary field covering:
- AI principles and practical applications
- Python programming
- Natural Language Processing in Python
- Large Language Models and Transformers
- Developing apps with orchestration tools like LangChain
- Vector databases using PineCone
- Creating AI-driven applications
Each topic builds on the previous one, and skipping steps can lead to confusion. For instance, applying large language models requires familiarity with Langchain—just as studying natural language processing can be overwhelming without basic Python coding skills.
So, we created the AI Engineer Bootcamp 2024 to provide the most effective, time-efficient, and structured AI engineering training available online.
This pioneering training program overcomes the most significant barrier to entering the AI Engineering field by consolidating all essential resources in one place.
Our course is designed to teach interconnected topics seamlessly—providing all you need to become an AI Engineer at a significantly lower cost and time investment than traditional programs.
The Skills
1. Intro to Artificial Intelligence
Structured and unstructured data, supervised and unsupervised machine learning, Generative AI, and foundational models—these familiar AI buzzwords; what exactly do they mean?
Why study AI? Gain deep insights into the field through a guided exploration that covers AI fundamentals, the significance of quality data, essential techniques, Generative AI, and the development of advanced models like GPT, Llama, Gemini, and Claude.
2. Python Programming
Mastering Python programming is essential to becoming a skilled AI developer—no-code tools are insufficient.
Python is a modern, general-purpose programming language suited for creating web applications, computer games, and data science tasks. Its extensive library ecosystem makes it ideal for developing AI models.
Why study Python programming?
Python programming will become your essential tool for communicating with AI models and integrating their capabilities into your products.
3. Intro to NLP in Python
Explore Natural Language Processing (NLP) and learn techniques that empower computers to comprehend, generate, and categorize human language.
Why study NLP?
NLP forms the basis of cutting-edge Generative AI models. This program equips you with essential skills to develop AI systems that meaningfully interact with human language.
4. Introduction to Large Language Models
This program section enhances your natural language processing skills by teaching you to utilize the powerful capabilities of Large Language Models (LLMs). Learn critical tools like Transformers Architecture, GPT, Langchain, HuggingFace, BERT, and XLNet.
Why study LLMs?
This module is your gateway to understanding how large language models work and how they can be applied to solve complex language-related tasks that require deep contextual understanding.
5. Building Applications with LangChain
LangChain is a framework that allows for seamless development of AI-driven applications by chaining interoperable components.
Why study LangChain?
Learn how to create applications that can reason. LangChain facilitates the creation of systems where individual pieces—such as language models, databases, and reasoning algorithms—can be interconnected to enhance overall functionality.
6. Vector Databases
With emerging AI technologies, the importance of vectorization and vector databases is set to increase significantly. In this Vector Databases with Pinecone module, you’ll have the opportunity to explore the Pinecone database—a leading vector database solution.
Why study vector databases?
Learning about vector databases is crucial because it equips you to efficiently manage and query large volumes of high-dimensional data—typical in machine learning and AI applications. These technical skills allow you to deploy performance-optimized AI-driven applications.
7. Speech Recognition with Python
Dive into the fascinating field of Speech Recognition and discover how AI systems transform spoken language into actionable insights. This module covers foundational concepts such as audio processing, acoustic modeling, and advanced techniques for building speech-to-text applications using Python.
Why study speech recognition?
Speech Recognition is at the core of voice assistants, automated transcription tools, and voice-driven interfaces. Mastering this skill enables you to create applications that interact with users naturally and unlock the full potential of audio data in AI solutions.
What You Get
- $1,250 AI Engineering training program
- Active Q&A support
- Essential skills for AI engineering employment
- AI learner community access
- Completion certificate
- Future updates
- Real-world business case solutions for job readiness
We're excited to help you become an AI Engineer from scratch—offering an unconditional 30-day full money-back guarantee.
With excellent course content and no risk involved, we're confident you'll love it.
Why delay? Each day is a lost opportunity. Click the ‘Buy Now’ button and join our AI Engineer program today.
01. Intro to AI Module Getting started
1. Building an AI tool in 5 minutes A quick demo
Intro to AI - Course notes PDF
Intro to AI - Course notes PDF
Intro to AI - Course notes PDF
3. Natural vs Artificial Intelligence
5. Demystifying AI, Data science, Machine learning, and Deep learning
02. Intro to AI Module Data is essential for building AI
1. Structured vs unstructured data 01:47
2. How we collect data 04:02
3. Labelled and unlabelled data 02:06
4. Metadata Data that describes data 01:42
03. Intro to AI Module Key AI techniques
1. Machine learning 06:15
2. Supervised, Unsupervised, and Reinforcement learning 05:34
3. Deep learning 08:27
04. Intro to AI Module Important AI branches
05. Intro to AI Module Understanding Generative AI
1. The rise of Gen AI: Introducing ChatGPT 02:09
2. Early approaches to Natural Language Processing (NLP) 02:42
3. Recent NLP advancements 03:01
4. From Language Models to Large Language Models (LLMs) 06:11
5. The efficiency of LLM training. Supervised vs Semi-supervised learning 03:35
6. From N-Grams to RNNs to Transformers: The Evolution of NLP 05:22
7. Phases in building LLMs 04:40
8. Prompt engineering vs Fine-tuning vs RAG: Techniques for AI optimization 04:24
9. The importance of foundation models 02:49
10. Buy vs Make: foundation models vs private models 02:36
06. Intro to AI Module Practical challenges in Generative AI
1. Inconsistency and hallucination 02:43
2. Budgeting and API costs 02:58
07. Intro to AI Module The AI tech stack
4. The importance of open source 06:10
08. Intro to AI Module AI job positions
09. Intro to AI Module Looking ahead
10. Python Module Why Python
1. Programming Explained in a Few Minutes 05:29
11. Python Module Setting Up the Environment
1. Jupyter - Introduction 03:28
2. Jupyter - Installing Anaconda 03:34
3. Jupyter - Introduction to Using Jupyter 04:53
4. Jupyter - Working with Notebook Files 04:30
5. Jupyter - Using Shortcuts 07:24
6. Jupyter - Handling Error Messages 05:52
7. Jupyter - Restarting the Kernel 02:03
Setting Up the Environment - Jupyter : 5 questions
12. Python Module Python Variables and Data Types
Python Variables and Types of Data_Exercises
Python Variables and Types of Data_Lectures
Python Variables and Types of Data_Solutions
2. Python Variables - Exercise #1
3. Python Variables - Exercise #2
4. Python Variables - Exercise #3
5. Python Variables - Exercise #4
7. Types of Data - Numbers and Boolean Values
8. Numbers and Boolean Values - Exercise #1
9. Numbers and Boolean Values - Exercise #2
10. Numbers and Boolean Values - Exercise #3
11. Numbers and Boolean Values - Exercise #4
12. Numbers and Boolean Values - Exercise #5
13. Types of Data - Numbers and Boolean Values
Python Variables and Types of Data_Exercises
Python Variables and Types of Data_Lectures
Python Variables and Types of Data_Solutions
Python Variables and Types of Data_Exercises1
Python Variables and Types of Data_Lectures2
Python Variables and Types of Data_Solutions3
13. Python Module Basic Python Syntax
1. Basic Python Syntax - Arithmetic Operators
Introduction to Using Basic Python’s Syntax_Exercises
Introduction to Using Basic Python’s Syntax_Lectures
Introduction to Using Basic Python's Syntax_Solutions
2.15 Arithmetic Operators - Exercise #1
3.16 Arithmetic Operators - Exercise #2
4.17 Arithmetic Operators - Exercise #3
5.18 Arithmetic Operators - Exercise #4
6.19 Arithmetic Operators - Exercise #5
7.20 Arithmetic Operators - Exercise #6
8.21 Arithmetic Operators - Exercise #7
9.22 Arithmetic Operators - Exercise #8
10.9 Basic Python Syntax - Arithmetic Operators
11. Basic Python Syntax - The Double Equality Sign
12.23 The Double Equality Sign - Exercise #1
13.10 Basic Python Syntax - The Double Equality Sign
14. Basic Python Syntax - Reassign Values
15.24 Reassign Values - Exercise #1
16.25 Reassign Values - Exercise #2
17.26 Reassign Values - Exercise #3
18.27 Reassign Values - Exercise #4
19.11 Basic Python Syntax - Reassign Values
20. Basic Python Syntax - Add Comments
21.12 Basic Python Syntax - Add Comments
22. Basic Python Syntax - Line Continuation
23.28 Line Continuation - Exercise #1
24. Basic Python Syntax - Indexing Elements
25.29 Indexing Elements - Exercise #1
26.30 Indexing Elements - Exercise #2
27.13 Basic Python Syntax - Indexing Elements
28. Basic Python Syntax - Indentation
Introduction to Using Basic Python’s Syntax_Exercises1
Introduction to Using Basic Python’s Syntax_Lectures2
Introduction to Using Basic Python's Syntax_Solutions3
29.31 Indentation - Exercise #1
30.14 Basic Python Syntax - Indentation
14. Python Module More on Operators
More on Working with Operators_Exercises
More on Working with Operators_Lectures
More on Working with Operators_Solutions
1. Operators - Comparison Operators
2.32 Comparison Operators - Exercise #1
3.33 Comparison Operators - Exercise #2
4.34 Comparison Operators - Exercise #3
5.35 Comparison Operators - Exercise #4
6.15 Operators - Comparison Operators
7. Operators - Logical and Identity Operators
8.36 Logical and Identity Operators - Exercise #1
9.37 Logical and Identity Operators - Exercise #2
10.38 Logical and Identity Operators - Exercise #3
11.39 Logical and Identity Operators - Exercise #4
12.40 Logical and Identity Operators - Exercise #5
13.41 Logical and Identity Operators - Exercise #6
14.16 Operators - Logical and Identity Operators
15. Python Module Conditional Statements
1. Conditional Statements - The IF Statement
If-Elif-Else Statements_Exercises
If-Elif-Else Statements_Lectures
If-Elif-Else Statements_Solutions
2.42 The IF Statement - Exercise #1
3.43 The IF Statement - Exercise #2
4.17 Conditional Statements - The IF Statement
5. Conditional Statements - The ELSE Statement
6.44 The ELSE Statement - Exercise #1
7. Conditional Statements - The ELIF Statement
8.45 The ELIF Statement - Exercise #1
9.46 The ELIF Statement - Exercise #2
10. Conditional Statements - A Note on Boolean Values
If-Elif-Else Statements_Exercises
If-Elif-Else Statements_Lectures
If-Elif-Else Statements_Solutions
11.18 Conditional Statements - A Note on Boolean Values
16. Python Module Functions
1. Functions - Defining a Function in Python
2. Functions - Creating a Function with a Parameter
3.47 Creating a Function with a Parameter - Exercise #1
4.48 Creating a Function with a Parameter - Exercise #2
5. Functions - Another Way to Define a Function
6.49 Another Way to Define a Function - Exercise #1
7. Functions - Using a Function in Another Function
8.50 Using a Function in Another Function - Exercise #1
9. Functions - Combining Conditional Statements and Functions
10.51 Combining Conditional Statements and Functions - Exercise #1
11. Functions - Creating Functions Containing a Few Arguments
12. Functions - Notable Built-in Functions in Python
13.52 Notable Built-in Functions in Python - Exercise #1
14.53 Notable Built-in Functions in Python - Exercise #2
15.54 Notable Built-in Functions in Python - Exercise #3
16.55 Notable Built-in Functions in Python - Exercise #4
17.56 Notable Built-in Functions in Python - Exercise #5
18.57 Notable Built-in Functions in Python - Exercise #6
19.58 Notable Built-in Functions in Python - Exercise #7
20.59 Notable Built-in Functions in Python - Exercise #8
21.60 Notable Built-in Functions in Python - Exercise #9
17. Python Module Sequences
9.66 Using Methods - Exercise #1
10.67 Using Methods - Exercise #2
11.68 Using Methods - Exercise #3
12.69 Using Methods - Exercise #4
13.21 Sequences - Using Methods
15.70 List Slicing - Exercise #1
16.71 List Slicing - Exercise #2
17.72 List Slicing - Exercise #3
18.73 List Slicing - Exercise #4
19.74 List Slicing - Exercise #5
20.75 List Slicing - Exercise #6
21.76 List Slicing - Exercise #7
28.81 Dictionaries - Exercise #1
29.82 Dictionaries - Exercise #2
30.83 Dictionaries - Exercise #3
31.84 Dictionaries - Exercise #4
32.85 Dictionaries - Exercise #5
33.86 Dictionaries - Exercise #6
34.22 Sequences - Dictionaries
18. Python Module Iteration
5. Iteration - While Loops and Incrementing
6.89 While Loops and Incrementing - Exercise #1
7. Iteration - Creatie Lists with the range() Function
8.90 Create Lists with the range() Function - Exercise #1
9.91 Create Lists with the range() Function - Exercise #2
10.92 Create Lists with the range() Function - Exercise #3
11.24 Iteration - Creatie Lists with the range() Function
12. Iteraion - Use Conditional Statements and Loops Together
13.93 Conditional Statements and Loops - Exercise #1
14.94 Conditional Statements and Loops - Exercise #2
15.95 Conditional Statements and Loops - Exercise #3
16. Iteration - Conditional Statements, Functions, and Loops
17.96 Conditional Statements, Functions, and Loops - Exercise #1
18. Iteration - Iterating over Dictionaries
19.97 Iterating over Dictionaries - Exercise #1
20.98 Iterating over Dictionaries - Exercise #2
19. Python Module A Few Important Python Concepts and Terms
1. Introduction to Object Oriented Programming (OOP)
2. Modules, Packages, and the Python Standard Library
4.25 Important Python Concepts and Terms
5. What is Software Documentation
20. NLP Module Introduction
2. Course materials and notebooks
5. Supervised vs unsupervised NLP
21. NLP Module Text Preprocessing
1. The importance of data preparation
4. Text for the customer_reviews variable
9. A note on the practical task
10. tripadvisor_hotel_reviews (microsoft excel file)
22. NLP Module Identifying Parts of Speech and Named Entities
2. 3.2 Parts of Speech (POS) Tagging
2. Parts of Speech (POS) tagging
2. Text for the emma_ja variable
3. 3.3 Named Entity Recognition
3. Named Entity Recognition (NER)
3. Text for the google_text variable
4. A note on the practical task
5. bbc_news (microsoft excel file)
23. NLP Module Sentiment Analysis
2. 4.2 Rule-based Sentiment Analysis
2. Rule-based sentiment analysis
3. 4.3 Pre-trained Transformer Models
3. Pre-trained transformer models
4. A note on the practical task
5. book_reviews_sample (microsoft excel file)
24. NLP Module Vectorizing Text
1. Numerical representation of text
25. NLP Module Topic Modelling
2. When to use topic modelling
3. Latent Dirichlet Allocation (LDA)
4. A note on the following lesson
6. Latent Semantic Analysis (LSA)
26. NLP Module Building Your Own Text Classifier
1. Building a custom text classifier
2. Building a Custom Classifier
3. Building a Custom Classifier
4. Building a Custom Classifier
4. Linear support vector machine
27. NLP Module Categorizing Fake News (Case Study)
3. Exploring our data through POS tags
6. Does sentiment differ between news types
7. What topics appear in fake news (Part 1)
8. What topics appear in fake news (Part 2)
9. Categorizing fake news with a custom classifier
28. NLP Module The Future of NLP
29. LLMs Module Introduction to Large Language Models
2. Course materials and notebooks
6. Pre-training and fine tuning
30. LLMs Module The Transformer Architecture
3. The solution attention is all you need
4. The transformer architecture
9. Predicting the final outputs
31. LLMs Module Getting Started With GPT Models
6. Key word text summarization
6. Text for the messages and prompt variables
7. Text for the messages variable
8. Introduction to LangChain in Python
10. Adding custom data to our chatbot
32. LLMs Module Hugging Face Transformers
5. Hugging Face and PyTorchTensorFlow
33. LLMs Module Question and Answer Models With BERT
3. Loading the model and tokenizer
3. Question and answer models with BERT
4. Text for the answer_document variable
6. Text for the sunset_motors_context variable
34. LLMs Module Text Classification With XLNet
2. A note on the following lecture
3. Text classification with XLNET
35. LangChain Module Introduction
2. Course materials and notebooks
3. Business applications of LangChain
4. What makes LangChain powerful
36. LangChain Module Tokens, Models, and Prices
37. LangChain Module Setting Up the Environment
1. Setting up a custom anaconda environment for Jupyter integration
2. Obtaining an OpenAI API key
3. Setting the API key as an environment variable
38. LangChain Module The OpenAI API
2. System, user, and assistant roles
3. Creating a sarcastic chatbot
4. Temperature, max tokens, and streaming
39. LangChain Module Model Inputs
5. Prompt templates and prompt values
6. Chat prompt templates and chat prompt values
7. Few-shot chat message prompt templates
40. LangChain Module Message History and Chatbot Memory
2. Conversation buffer memory Implementing the setup
3. Conversation buffer memory Configuring the chain
4. Conversation buffer window memory
5. Conversation summary memory
41. LangChain Module Output Parsers
2. Comma-separated list output parser
42. LangChain Module LangChain Expression Language (LCEL)
1. Piping a prompt, model, and an output parser
4. The Runnable and RunnableSequence classes
5. Piping chains and the RunnablePassthrough class
8. Piping a RunnableParallel with other Runnables
11. Adding memory to a chain (Part 1) Implementing the setup
12. RunnablePassthrough with additional keys
14. Adding memory to a chain (Part 2) Creating the chain
43. LangChain Module Retrieval Augmented Generation (RAG)
1. How to integrate custom data into an LLM
3. Introduction to document loading and splitting
4. Introduction to document embedding
5. Introduction to document storing, retrieval, and generation
6. Indexing Document loading with PyPDFLoader
6. Introduction_to_Data_and_Data_Science
7. Indexing Document loading with Docx2txtLoader
7. Introduction_to_Data_and_Data_Science
8. Indexing Document splitting with character text splitter (Theory)
9. Indexing Document splitting with character text splitter (Code along)
10. Indexing Document splitting with Markdown header text splitter
10. Introduction_to_Data_and_Data_Science_2
11. Indexing Text embedding with OpenAI
12. Indexing Creating a Chroma vectorstore
13. Indexing Inspecting and managing documents in a vectorstore
14. Retrieval Similarity search
15. Retrieval Maximal Marginal Relevance (MMR) search
16. Retrieval Vectorstore-backed retriever
17. Generation Stuffing documents
18. Generation Generating a response
44. LangChain Module Tools and Agents
1. Introduction to reasoning chatbots
2. Tools, toolkits, agents, and agent executors
3. Fixing the GuessedAtParserWarning
4. Creating a Wikipedia tool and piping it to a chain
5. Creating a retriever and a custom tool
7. Creating a tool calling agent and an agent executor
8. AgentAction and AgentFinish
45. Vector Databases Module Introduction
2. Course materials and notebooks
3. Database comparison SQL, NoSQL, and Vector
4. Understanding vector databases
46. Vector Databases Module Basics of Vector Space and High-Dimensional Data
1. Introduction to vector space
2. Distance metrics in vector space
3. Vector embeddings walkthrough
47. Vector Databases Module Introduction to The Pinecone Vector Database
1. Vector databases, comparison
2. Pinecone registration, walkthrough and creating an Index
3. Connecting to Pinecone using Python
4. 3.5 Pinecone Homework solution
5. 3.3 Introduction to Pinecone
5. Creating and deleting a Pinecone index using Python
6. 3.4 Introduction to Pinecone
6. Upserting data to a pinecone vector database
7. Getting to know the fine web data set and loading it to Jupyter
8. Upserting data from a text file and using an embedding algorithm
48. Vector Databases Module Semantic Search with Pinecone and Custom (Case Study)
1. Introduction to semantic search
2. Introduction to the case study – smart search for data science courses
3. Getting to know the data for the case study
4. Data loading and preprocessing
5. 4. 365 Courses Vector Store Data Preprocessing
5. Pinecone Python APIs and connecting to the Pinecone server
7. 4. 365 Courses Vector Store Creating Index
7. Embedding the data and upserting the files to Pinecone
8. 4. 365 Courses Vector Store Embedding
8. Similarity search and querying the data
9. How to update and change your vector database
10. course_section_descriptions
10. Data preprocessing and embedding for courses with section data
11. 4.10 Pinecone Semantic Search Course Description weighted assignment
12. 4. 365 Courses and Sections Semantic Search Embedding
12. Upserting the new updated files to Pinecone
13. 4. 365 Courses and Sections Semantic Search Upserting the data
13. Similarity search and querying courses and sections data
14. Pinecone Semantic Search Course Section Weighted
15. 4. 365 Courses and Sections Semantic Search
15. Using the BERT embedding algorithm
16. Vector database for recommendation engines
17. Vector database for semantic image search
18. Vector database for biomedical research
49. Speech Recognition Module Introduction
1. Welcome to the world of Speech Recognition
4. How it all started Formants, harmonics, and phonemes
50. Speech Recognition Module Sound and Speech Basics
1. How do humans recognize speech
2. Fundamentals of sound and sound waves
51. Speech Recognition Module Analog to Digital Conversion
1. Key concepts Sample Rate, bit depth, and bit rate
2. Audio signal processing for Machine Learning and AI
52. Speech Recognition Module Audio Feature Extraction for AI Applications
2. Frequency-domain and time-frequency-domain audio features
3. Time-domain feature extraction Framing and feature computation
4. Frequency-domain feature extraction Fourier transform
53. Speech Recognition Module Technology Mechanics
1. Acoustic and language modeling
2. Hidden Markov Models (HMMs) and traditional neural networks
3. Deep learning models CNNs, RNNs, and LSTMs
4. Advanced speech recognition systems Transformers
5. Building a speech recognition model part I
6. Building a speech recognition model part II
7. Selecting the appropriate speech recognition tool
8. Expanding beyond the tools we've covered
54. Speech Recognition Module Setting Up the Environment
2. Setting up a new environment
3. Installing packages for speech recognition
3. Setting Up Packages for Speech Recognition
4. Importing the relevant packages in Jupyter
55. Speech Recognition Module Transcribing Audio with Google Web Speech API
1. Audio file formats for speech recognition
2. Importing audio files in Jupyter Notebook
2. Speech Recognition with Python
3. The SpeechRecognition library Google Web Speech API
4. Evaluation metrics WER and CER
5. Calculating WER and CER in Python
5. ground_truth
"""My name is Ivan and I am excited to have you as part of our learning community!
Before we get started, I’d like to tell you a little bit about myself. I’m a sound engineer turned data scientist,
curious about machine learning and Artificial Intelligence. My professional background is primarily in media production,
with a focus on audio, IT, and communications"""
